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4,299,418 works, Canadian by any of four routes.

Every filter state is a URL; the URL is the query; the query is citable via /q/⟨hash⟩. The page, the API and the export parse the same parameters.

The current cohort, streamed from the database: every work column, the machine labels, the provisional scores, and the per-row validation status. Exports are capped at 100,000 rows. Mints a permanent /q/ link for this exact query. The same filters always produce the same link, whoever asks.

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Online and Blended Learning
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Direct Codex and Gemma labels are unvalidated and sparse. Distilled predictions cover the full frame and are also unvalidated. Choose the evidence source explicitly; absence of a direct label is never a negative label.

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The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

3,545 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
3,545 works in the cohort · of 4,299,418page 57 of 71

Labels cover 14 of 3,545 works in this cohort. The rest are unlabeled, which is not a negative label: the label table is sparse today and grows as labeling rounds land.

Distilled predictions cover 3,545 of 3,545 works in this cohort. Predictions are machine_predicted_unvalidated. The Gemma side is a direct model label for every work (title-only); the Codex side is a distilled, calibrated classifier. Candidate is the union; consensus is the intersection.

affno abstractunlabeled
Redesigning the Curriculum for E-learning
Dorian Stoilescu
2004· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Student Retention and Online Seminars
Peter Kiriakidis, Mary Brown
2008· article· en· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
E-Learning at Riverview Health Centre
Helena M. Wall
2006· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Social Sciences
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
aboutno affunlabeled
Summary
2015· article· en· Social Sciences
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affno abstractunlabeled
Blended Learning Across the Nursing Lifespan
Sarah Tougas
2018· article· en· MacEwan University Student Research Proceedings· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
The match game: Instructional design for every learner
Patricia McGee, Uli Rauch, Colleen Carmean, Cyprien Lomas
2005· article· en· University of Washington Tacoma Digital Commons (University of Washington Tacoma)· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Learning in a Virtual World, Part 1
Patricia McGee, Colleen Carmean, Ulrich Rauch, Nick Noakes, Cyprien Lomas
2007· article· en· EdMedia: World Conference on Educational Media and Technology· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About